Pip install the ultralytics package including all requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

pip install ultralytics

YOLOv8 may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"

yolo can be used for a variety of tasks and modes and accepts additional arguments, i.e. imgsz=640. See a full list of available yolo arguments in the YOLOv8 Docs.

yolo task=detect    mode=train    model=yolov8n.pt        args...
          classify       predict        yolov8n-cls.yaml  args...
          segment        val            yolov8n-seg.yaml  args...
                         export         yolov8n.pt        format=onnx  args...

YOLOv8 may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
results = model.train(data="coco128.yaml", epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
success = model.export(format="onnx")  # export the model to ONNX format

Models download automatically from the latest Ultralytics release.

Known Issues / TODOs

We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we will submit to arxiv.org once complete.

• TensorFlow exports
• DDP resume
• arxiv.org paper

See Detection Docs for usage examples with these models.

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo mode=val task=detect data=coco.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu

See Segmentation Docs for usage examples with these models.

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo mode=val task=segment data=coco.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu

See Classification Docs for usage examples with these models.

• acc values are model accuracies on the ImageNet dataset validation set.
  Reproduce by yolo mode=val task=classify data=path/to/ImageNet device=0
• Speed averaged over ImageNet val images using an Amazon EC2 P4d instance.
  Reproduce by yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu